A novel framework for process pattern construction and multi-mode monitoring is proposed. To identify process patterns, the framework utilizes a clustering method that consists of an ensemble moving window strategy along with an ensemble clustering solutions strategy. A new k-independent component analysis-principal component analysis (k-ICA-PCA) modeling method captures the relevant process patterns in corresponding clusters and facilitates the validation of ensemble solutions. Following pattern construction, the proposed framework offers an adjoined multi-ICA-PCA model for detection of faults under multiple operating modes. The Tennessee Eastman (TE) benchmark process is used as a case study to demonstrate the salient features of the method. Specifically, the proposed method is shown to have superior performance compared to the previously reported k-PCA models clustering approach. (C) 2011 Elsevier Ltd. All rights reserved.